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首页> 外文期刊>IEEE Transactions on Signal Processing >Adapting the Number of Particles in Sequential Monte Carlo Methods Through an Online Scheme for Convergence Assessment
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Adapting the Number of Particles in Sequential Monte Carlo Methods Through an Online Scheme for Convergence Assessment

机译：通过在线收敛性评估方案，采用顺序蒙特卡洛方法调整粒子数

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摘要

Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the filter is guaranteed when the number of particles tends to infinity, the quality of the approximation is usually unknown but strongly dependent on the number of particles. In this paper, we propose a novel method for assessing the convergence of particle filters in an online manner, as well as a simple scheme for the online adaptation of the number of particles based on the convergence assessment. The method is based on a sequential comparison between the actual observations and their predictive probability distributions approximated by the filter. We provide a rigorous theoretical analysis of the proposed methodology and, as an example of its practical use, we present simulations of a simple algorithm for the dynamic and online adaptation of the number of particles during the operation of a particle filter on a stochastic version of the Lorenz 63 system.
机译：粒子过滤器广泛地用于通过一组加权粒子来近似状态空间模型中隐藏状态的后验分布。当粒子数趋于无穷大时，虽然可以保证滤波器的收敛性，但是近似质量通常是未知的，但在很大程度上取决于粒子数。在本文中，我们提出了一种在线评估粒子过滤器收敛性的新方法，以及基于收敛性评估的粒子数量在线自适应的简单方案。该方法基于实际观测值与过滤器近似的预测概率分布之间的顺序比较。我们对提出的方法进行了严格的理论分析，并以其实际应用为例，介绍了一种简单算法的仿真，该算法用于在随机版本的粒子滤波器上运行粒子过滤器时动态地在线调整粒子数量Lorenz 63系统。

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